The focus of this project is development and refinement of statistical procedures for the design and analysis of cancer screening and related studies. Problems under investigation include derivation and comparison of data analysis methods, assessment of case-control studies for screening evaluation, development of models of cancer screening, and approaches to the analysis of categorical data. To assess the case-- control design for screening evaluation, simulation models are being developed to provide population data with and without screening. Case-control studies are then done in the screened populations and the results compared with the true effect to assess bias in the case-control approach. A model was developed to estimate dilution in a screening trial where followup continues after screening has ended. This was applied to both the Overall Analysis of all randomized individuals and a Limited Analysis of restricted subgroups of cases defined at certain intervals from study entry. Criteria were developed for comparability of the restricted case subgroups used in the Limited Analysis. Data from diagnostic testing and screening can often be analyzed using techniques for missing categorical data. Simple techniques have been developed for obtaining closed-form maximum likelihood estimates and their asymptotic variances and for finding the observed information matrix when using the EM algorithm with missing categorical data. A symbolic method has been developed for determining identifiable models in experimental designs involving missing categorical data. A general multinomial to Poisson transformation was developed for likelihood inference. Methods have also been developed for analyzing data from randomized screening trials to estimate the benefit of screening unaffected by lead time bias and also the average lead time. Approaches were defined for data monitoring of cancer screening trials.